Primary central nervous system (“CNS”) tumors, such as gliomas, are so named by the types of cells they contain, their location, or both. Detection of such tumors is difficult because of the diverse symptoms that patients with CNS tumors may present. Furthermore, once a CNS tumor is suspected, expensive imaging techniques are generally required for diagnosis, these various imaging techniques all suffer from one or more inadequacies, and none provide any definitive prognostic value. Laboratory tests such as electroencephalograms provide information on brain activity that might indicate a tumor, but are not able to identify and characterize CNS tumors or their prognosis.
Malignant gliomas are the most common primary brain tumor, and are classified histologically, with pathological diagnosis affecting prognostic estimation and therapeutic decision-making more than any other variable. (Nutt et al., Cancer Research 63:1602-1607 (2003)) Oligodendrogliomas, which are often chemosensitive, have a more favorable prognosis than glioblastomas, which are resistant to most available therapies. (Nutt, supra).
The maturation of microarray technology has enabled the routine collection of genome-wide gene expression (RNA) data. In cancer diagnostics, several authors have shown that microarray data collected from tumors may be useful in differential diagnosis, tumor staging, and prognosis. The data produced by these studies ideally represents a valuable resource for the development of new diagnostics. However, at present, the application of microarray technology requires steps in sample collection and sample preparation that inhibit routine clinical adoption.
In contrast, DNA-based markers are commonly used in cancer diagnostics. Diagnostic implementations utilizing fluorescence in situ hybridization (FISH) and RT-PCR technology are in widespread use. New diagnostic products based on such accepted technology will more quickly find clinical acceptance.
It is established that specific genetic aberrations are often associated with clinical characteristics. Examples include the association of 1 p/19 q deletions in breast cancer with improved response to chemotherapy, and the association of 8 q gain with poor prognosis in prostate cancer. Such aberrations have been detected with comparative genomic hybridization (“CGH”). However, the relationship between tumor karyotype and phenotype is often subtle, and may be difficult to determine from the typically available datasets consisting of low-resolution CGH data collected from a small number of samples.
Several studies have demonstrated the association of genetic aberrations with gene expression changes. In independent studies, Hyman et al (2002) and Pollack et al (2002) both found a strong relationship between high amplification and high expression in breast tumors. Crawley et al (2002) has reported on a data analysis method that accurately predicts regions of copy number aberrations in hepatocellular carcinomas using only gene expression data. These investigations support the notion that gene expression data can be used as a window to the underlying genetic defects, and thus the idea that a combined analysis of gene expression data and CGH copy number data with the aim of identifying DNA markers is viable.